Self Organizing Networks (SON)

Self-learning, location-aware SON

Massive non-uniformity in cellular networks— constant changes in subscriber behaviors—video usage exploding—RAN teams needing to deliver more with less. These are just some of the challenges facing mobile operators and managed service providers. As the complexity of networks increases and an insatiable demand for mobile broadband continues, the need for SON has never been greater.
SON is not just about the network. It’s about the customer and the quality of experience (QoE) they receive. Deployed correctly, operators can get significant improvements in performance, better service quality, and increased revenues—all with fewer resources and minimum user touch.

GEOson – Self-learning, location-aware

GEOson is a unique, scalable, real-time SON solution that models decisions based on:

Subscriber and network insight

Location intelligence

Real-time self-learning

By combining the power of ariesoGEO with the value of InteliSON, GEOson significantly improves network efficiency, performance, and customer QoE—accomplishments that are unattainable by manual means.

Your network will be more efficient

You will use fewer resources and reduce OpEx

Your CapEx can be deferred

Your customers will get better services

Configuration, Optimization, Healing

To deliver the self-configuration, self-optimization, and self-healing requirements of SON, GEOson uses network, subscriber, and location insight to build a self-learning SON solution.

Subscriber
Network optimization is all about the subscriber. GEOson uses real customer insight, capturing all events from all subscribers all the time. SON results can then reflect subscriber QoE and revenues. Resources can be balanced, and GEOson can adapt in real time to subscribers’ needs while meeting network requirements.

Network
Network performance statistics correlated with subscriber data is the foundation for the intelligence that feeds GEOson. We interface directly to elements as well as NEM management systems: performance, configuration, fault, and event trace OSS subsystems. In addition, our Hybrid SON solution leverages any D-SON solution you may already have deployed.

Location
With building-level accuracy, we deliver unprecedented insight into subscriber usage patterns. SON benefits greatly from granular, location-aware insight, and GEOson handles that massive non-uniformity in the network.

Self-Learning
The initial stage of self-learning is to automatically and quickly set initial thresholds and parameters. With the massive non-uniformity in networks, you cannot use a single group of settings and apply them broadly across a network. By measuring and monitoring a broad set of insights (such as subscribers and locations) across the network over time, GEOson can adapt to changing patterns and usage to continually optimize the network. As the network evolves and GEOson continues to optimize its performance, the optimization parameters, thresholds, and partitioning into optimization clusters to maintain optimal service may require changes.